Skip Navigation



Bioinformatics Advance Access published online on May 14, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth308
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
20/16/2676    most recent
bth308v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Wong, P. W. H.
Right arrow Articles by Yiu, S. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wong, P. W. H.
Right arrow Articles by Yiu, S. M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received October 20, 2003
Revised April 13, 2004
Accepted April 28, 2004

Article

An efficient algorithm for optimizing whole genome alignment with noise

Prudence W. H. Wong 1*, T. W. Lam 1, N. Lu 1, H. F. Ting 1, S. M. Yiu 1

1 Department of Computer Science, University of Hong Kong, Hong Kong

* To whom correspondence should be addressed. E-mail: whwong{at}cs.hku.hk.


  Abstract

Motivation: This paper is concerned with algorithms for aligning two whole genomes so as to identify regions that possibly contain conserved genes. Motivated by existing heuristic-based software tools, we initiate the study of an optimization problem that attempts to uncover conserved genes with a global concern. Another interesting feature in our formulation is the tolerance of noise, which also complicates the optimization problem. A brute-force approach takes time exponential in the noise level.

Results: We show how an insight into the optimization structure can lead to a drastic improvement in the time and space requirement (precisely, to O(k2n2) and O(k2n), respectively, where n is the size of the input and k is the noise level). The reduced space requirement allows us to implement the new algorithm, called MaxMinCluster, on a PC. It is exciting to see that when tested with different real data sets, MaxMinCluster consistently uncovers a high percentage of conserved genes that have been published by GenBank. Its performance is indeed favorably compared to MUMmer (perhaps the most popular software tool for uncovering conserved genes in a whole-genome scale).

Availability: The source code is available from the website http://www.csis.hku.hk/~colly/maxmincluster/. Detailed proof of the propositions can also be found there.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
H. L. Chan, T. W. Lam, W. K. Sung, P. W. H. Wong, S. M. Yiu, and X. Fan
The mutated subsequence problem and locating conserved genes
Bioinformatics, May 15, 2005; 21(10): 2271 - 2278.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.